Dataset Information

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

ABSTRACT:

Background

Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior.

Aims

It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity.

Method

We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data.

Results

We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls.

Conclusions

Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

SUBMITTER: Zuhlsdorff K

PROVIDER: S-EPMC10755559 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

Zühlsdorff Katharina K Verdejo-Román Juan J Clark Luke L Albein-Urios Natalia N Soriano-Mas Carles C Cardinal Rudolf N RN Robbins Trevor W TW Dalley Jeffrey W JW Verdejo-García Antonio A Kanen Jonathan W JW

BJPsych open 20231211 1

<h4>Background</h4>Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior.<h4>Aims</h4>It is poorly understood whether impairments in flexibility differ between individuals with coc ...[more]

PMID: 38073280

Similar Datasets

Project description:Gambling disorder (GD) is a behavioral addiction associated with impairments in value-based decision-making and behavioral flexibility and might be linked to changes in the dopamine system. Maximizing long-term rewards requires a flexible trade-off between the exploitation of known options and the exploration of novel options for information gain. This exploration-exploitation trade-off is thought to depend on dopamine neurotransmission. We hypothesized that human gamblers would show a reduction in directed (uncertainty-based) exploration, accompanied by changes in brain activity in a fronto-parietal exploration-related network. Twenty-three frequent, non-treatment seeking gamblers and twenty-three healthy matched controls (all male) performed a four-armed bandit task during functional magnetic resonance imaging (fMRI). Computational modeling using hierarchical Bayesian parameter estimation revealed signatures of directed exploration, random exploration, and perseveration in both groups. Gamblers showed a reduction in directed exploration, whereas random exploration and perseveration were similar between groups. Neuroimaging revealed no evidence for group differences in neural representations of basic task variables (expected value, prediction errors). Our hypothesis of reduced frontal pole (FP) recruitment in gamblers was not supported. Exploratory analyses showed that during directed exploration, gamblers showed reduced parietal cortex and substantia-nigra/ventral-tegmental-area activity. Cross-validated classification analyses revealed that connectivity in an exploration-related network was predictive of group status, suggesting that connectivity patterns might be more predictive of problem gambling than univariate effects. Findings reveal specific reductions of strategic exploration in gamblers that might be linked to altered processing in a fronto-parietal network and/or changes in dopamine neurotransmission implicated in GD.SIGNIFICANCE STATEMENT Wiehler et al. (2021) report that gamblers rely less on the strategic exploration of unknown, but potentially better rewards during reward learning. This is reflected in a related network of brain activity. Parameters of this network can be used to predict the presence of problem gambling behavior in participants.

Dataset Information

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

Background

Aims

Method

Results

Conclusions

Publications

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets